Herein, when referring to ethics, we are namely focusing on the field of AI and in particular machine learning. The Ethics of AI [1]} has become a `hot topic' for researchers both internal and external to the AI community, with the peer-review process for publications itself being placed under scrutiny [2]}.
The Ethics of AI and Machine Ethics [3]} are quite different terms for the field of AI. The latter refers to giving conscious ethical based decision making power to machines. The former although somewhat informing the latter, refers more broadly to decisions made by the researchers involved, covering diversity and representation, e. g.  to avoid discrimination [4]}, or inherent latent biases that may come from archival databases [5]}. As AI is largely data-driven, understanding its environment through context provided via annotations, Big Data ethics for AI algorithms is an expanding discussion point [6]}, [7]}. In this regard, crowdsourced data (i. e.  data gathered from large amounts of paid or unpaid individuals via the internet) – made by humans with little time and minimal interest in the annotation task – is one aspect which is raising concern, due to the resulting validity of such annotations [8]}.
